Instructions to use Avdpro/DeepSeek-V4-Flash-SSD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Avdpro/DeepSeek-V4-Flash-SSD with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir DeepSeek-V4-Flash-SSD Avdpro/DeepSeek-V4-Flash-SSD
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
AI2Apps SSD-ready checkpoint
This is a byte-preserving storage-layout conversion of
deepseek-ai/DeepSeek-V4-Flash at immutable revision 60d8d70770c6776ff598c94bb586a859a38244f1.
Original tensor values and quantization are retained. Original routed experts
are externalized into the experts/ directory rather than duplicated in the
backbone safetensors.
Requires an AI2Apps Runtime with explicit deepseek-v4-expert-major-v1 support.
This candidate is not a drop-in checkpoint for unmodified Transformers,
mlx-lm or mlx-vlm. Do not use the backbone safetensors alone.
The corresponding Runtime and model Package have not yet completed release
acceptance. Full/Cached engine compatibility is recorded separately in the
Runtime release receipt; the presence of a reversible tensor map alone is
not an end-to-end engine guarantee.
ssd-checkpoint.json: format, provenance and file digests.external-tensors.json: original tensor names and external byte locations.source-tensor-sha256.json: original tensor payload digests verified during export.model.safetensors.index.json: ordinary/vision/other retained tensor index.experts/: complete routed expert payloads, with no re-quantization.
See LICENSE and README.upstream.md for upstream terms and attribution.
This storage format changes installation space and data access; it is not a
new model training or a claim of improved model accuracy.
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